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Learning a bayesian network from ordinal data

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  • Flaminia Musella
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    Abstract

    Bayesian networks are graphical models that represent the joint distributionof a set of variables using directed acyclic graphs. When the dependence structure is unknown (or partially known) the network can be learnt from data. In this paper, we propose a constraint-based method to perform Bayesian networks structural learning in presence of ordinal variables. The new procedure, called OPC, represents a variation of the PC algorithm. A nonparametric test, appropriate for ordinal variables, has been used. It will be shown that, in some situation, the OPC algorithm is a solution more efficient than the PC algorithm.

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    File URL: http://dipeco.uniroma3.it/public/WP%20139%20Musella%202011.pdf
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    Bibliographic Info

    Paper provided by Department of Economics - University Roma Tre in its series Departmental Working Papers of Economics - University 'Roma Tre' with number 0139.

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    Date of creation: Oct 2011
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    Handle: RePEc:rtr:wpaper:0139

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    Related research

    Keywords: Structural Learning; Monotone Association; Nonparametric Methods;

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